Andrew D. Kern

EcoLocator, a deep learning model, predicts geographic and climate origins from genotypes, outperforming traditional methods and showing practical applicability in conservation.

This study examines genome evolution in the Pacific acorn barnacle, showing high polymorphism and evidence of positive selection in large populations.

This paper presents a protocol for non-invasively determining the sex of cephalopods using skin swabs and qPCR, applicable to multiple species as early as 3 hours post-hatching.

MKado performs McDonald-Kreitman tests of natural selection using aligned coding sequences, offering extensions and visualization tools.

pg_gpu accelerates population genetics statistics on GPUs, achieving significant speedups over CPU implementations while maintaining accuracy.

The paper analyzes population structure in Culex pipiens using genomic data from a WNV hotspot, finding that ecotypes, Wolbachia infection, and habitat type drive genetic variation.

This study compares population genomes of two sympatric octopus species to examine recent evolutionary divergence and selection patterns.

This paper introduces neural posterior estimation for population genetics, showing it provides accurate and efficient posterior distributions compared to existing methods.

The paper introduces cxt, a transformer-based model that predicts coalescence times from mutation patterns, achieving performance comparable to MCMC methods and enabling efficient inference on genomic data.

This paper develops a non-invasive method to genotype cephalopod sex using quantitative PCR, validated across multiple species and sequencing data.

This study uses bee-collected pollen sequence data to train machine learning models for predicting sample origin, finding that Random Forest and k-Nearest Neighbors models achieve high accuracy.

The paper introduces a simulation-based CKMR method, CKMRnn, that improves population size estimation by accounting for spatial heterogeneity and demonstrates its accuracy using Ugandan elephant data.

The paper extends the stdpopsim simulation framework to include realistic selection models, enabling studies of selection's impact on genetic variation across species.

The paper uses spatial simulations to detect selective sweeps in Anopheles gambiae and identifies new variants under selection.

This paper provides a guide to implementing individual-based simulations in continuous landscapes, focusing on population dynamics and ecological applications.

This study presents the first chromosome-scale genome of a female California two-spot octopus, revealing a ZO sex chromosome system and demonstrating the ancient origin of the sex chromosome in cephalopods.

This paper introduces a neural network method to estimate spatial demographic maps from SNP data, showing it can infer population density and dispersal rates across landscapes.

disperseNN2 estimates dispersal distance from georeferenced polymorphism data, outperforming existing methods by 33-48% in error reduction.

The paper expands the stdpopsim species catalog and improves genome simulations by adding non-crossover recombination and species-specific annotations, making simulations more realistic for non-model organisms.

This paper develops a machine learning model to detect incomplete lineage sorting in species trees using genome-scale summary statistics, showing it can predict discordance and infer correct species tree topology.

The paper presents a single-cell molecular atlas of the octopus optic lobe, identifying neuronal and non-neuronal cell types and their spatial organization.

The paper introduces msprime 1.0, a high-performance simulation tool for ancestry and mutation in population genetics, showing it is faster and more memory efficient than existing alternatives.

This paper uses variational autoencoders to visualize population genetic structure, showing better global geometry preservation than t-SNE and UMAP.

The paper analyzes genome variation in 1,142 Anopheles mosquitoes to study population structure and insecticide resistance.

The paper introduces a deep learning method called Locator to predict geographic locations from genetic variation, achieving high accuracy and speed across different species.

This study investigates how continuous spatial structure affects population genetic analysis, finding biases in summary statistics, demographic inference, and GWAS when using discrete models.

The paper sequences genomes of 765 malaria vector mosquitoes to study genetic diversity and insecticide resistance.

The paper investigates the effects of two chromosomal inversions on transcriptional variation in Drosophila melanogaster using genomic and expression data.